Exploring Author, Article, and Venue Feature Sets for Rising Star Prediction in Academic Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rising stars are the researchers who are relatively new to the research area and have published fewer research articles, but their research work is of such standard that they have the potential to be top researchers in near future. Research work on the evaluation of researchers and prediction of rising stars is getting attention because it can be useful for selecting capable candidates for the jobs, hiring young faculty members for institutes, and seeking reviewers for journals and conferences and members for different committees. In this research study, the authors address the research problem of finding rising stars and propose novel features in diverse feature sets of three categories: article, author, and venue. The real-world data set has been extracted, preprocessed, and used from the Web of Science for empirical analysis. Several diverse supervised machine learning, ensemble learning algorithms, and deep learning are applied to the data set. The results, using classifiers, are compared based on standard performance evaluation measures to reveal the significance of the proposed as well as existing features. It also shows that the novel features play a significant role in finding rising stars. The ensemble-based machine learning classifier generalized linear model outperforms all other classifiers and gives the highest accuracy and F-measure compared to other models and the existing studies in the relevant literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.009 | 0.060 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it